One issue that has already been frequently pointed out is the slowness of learning in deep RL, that is, its demand for large amounts of data. DQN, for example, required much more experience to reach human-level performance in Atari games than would be required by an actual human learner [1]}. This issue is more complicated than it at first sounds, both because standard deep RL algorithms have become progressively more sample efficient, through alternative approaches like meta-learning and deep RL based on episodic memory [2]}, [3]}, and because human learners bring to bear a lifetime of prior experiences to each new learning problem.
